# Machine Learning in Real World: C4.5

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Machine Learning in Real World: C4.5

Outline Handling Numeric Attributes Dealing with Missing Values
Finding Best Split(s) Dealing with Missing Values Pruning Pre-pruning, Post-pruning, Error Estimates From Trees to Rules

Industrial-strength algorithms
For an algorithm to be useful in a wide range of real-world applications it must: Permit numeric attributes Allow missing values Be robust in the presence of noise Be able to approximate arbitrary concept descriptions (at least in principle) Basic schemes need to be extended to fulfill these requirements witten & eibe

C4.5 History ID3, CHAID – 1960s C4.5 innovations (Quinlan):
permit numeric attributes deal sensibly with missing values pruning to deal with for noisy data C4.5 - one of best-known and most widely-used learning algorithms Last research version: C4.8, implemented in Weka as J4.8 (Java) Commercial successor: C5.0 (available from Rulequest)

Numeric attributes Standard method: binary splits
E.g. temp < 45 Unlike nominal attributes, every attribute has many possible split points Solution is straightforward extension: Evaluate info gain (or other measure) for every possible split point of attribute Choose “best” split point Info gain for best split point is info gain for attribute Computationally more demanding witten & eibe

Weather data – nominal values
Outlook Temperature Humidity Windy Play Sunny Hot High False No True Overcast Yes Rainy Mild Normal If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes If none of the above then play = yes witten & eibe

Weather data - numeric Outlook Temperature Humidity Windy Play Sunny
85 False No 80 90 True Overcast 83 86 Yes Rainy 75 If outlook = sunny and humidity > 83 then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity < 85 then play = yes If none of the above then play = yes

Example Split on temperature attribute:
E.g. temperature  71.5: yes/4, no/2 temperature  71.5: yes/5, no/3 Info([4,2],[5,3]) = 6/14 info([4,2]) + 8/14 info([5,3]) = bits Place split points halfway between values Can evaluate all split points in one pass! Yes No Yes Yes Yes No No Yes Yes Yes No Yes Yes No witten & eibe

Avoid repeated sorting!
Sort instances by the values of the numeric attribute Time complexity for sorting: O (n log n) Q. Does this have to be repeated at each node of the tree? A: No! Sort order for children can be derived from sort order for parent Time complexity of derivation: O (n) Drawback: need to create and store an array of sorted indices for each numeric attribute witten & eibe

More speeding up Entropy only needs to be evaluated between points of different classes (Fayyad & Irani, 1992) value class Yes No Yes Yes Yes No No Yes Yes Yes No Yes Yes No X Potential optimal breakpoints Breakpoints between values of the same class cannot be optimal

Binary vs. multi-way splits
Splitting (multi-way) on a nominal attribute exhausts all information in that attribute Nominal attribute is tested (at most) once on any path in the tree Not so for binary splits on numeric attributes! Numeric attribute may be tested several times along a path in the tree Disadvantage: tree is hard to read Remedy: pre-discretize numeric attributes, or use multi-way splits instead of binary ones witten & eibe

Missing as a separate value
Missing value denoted “?” in C4.X Simple idea: treat missing as a separate value Q: When this is not appropriate? A: When values are missing due to different reasons Example 1: gene expression could be missing when it is very high or very low Example 2: field IsPregnant=missing for a male patient should be treated differently (no) than for a female patient of age 25 (unknown)

Split instances with missing values into pieces A piece going down a branch receives a weight proportional to the popularity of the branch weights sum to 1 Info gain works with fractional instances use sums of weights instead of counts During classification, split the instance into pieces in the same way Merge probability distribution using weights witten & eibe

Pruning Goal: Prevent overfitting to noise in the data
Two strategies for “pruning” the decision tree: Postpruning - take a fully-grown decision tree and discard unreliable parts Prepruning - stop growing a branch when information becomes unreliable Postpruning preferred in practice—prepruning can “stop too early”

Prepruning Based on statistical significance test
Stop growing the tree when there is no statistically significant association between any attribute and the class at a particular node Most popular test: chi-squared test ID3 used chi-squared test in addition to information gain Only statistically significant attributes were allowed to be selected by information gain procedure witten & eibe

Early stopping a b class 1 2 3 4 Pre-pruning may stop the growth process prematurely: early stopping Classic example: XOR/Parity-problem No individual attribute exhibits any significant association to the class Structure is only visible in fully expanded tree Pre-pruning won’t expand the root node But: XOR-type problems rare in practice And: pre-pruning faster than post-pruning witten & eibe

Post-pruning First, build full tree Then, prune it
Fully-grown tree shows all attribute interactions Problem: some subtrees might be due to chance effects Two pruning operations: Subtree replacement Subtree raising Possible strategies: error estimation significance testing MDL principle witten & eibe

Subtree replacement, 1 Bottom-up
Consider replacing a tree only after considering all its subtrees Ex: labor negotiations witten & eibe

Subtree replacement, 2 What subtree can we replace?

Subtree replacement, 3 Bottom-up
Consider replacing a tree only after considering all its subtrees witten & eibe

X *Subtree raising Delete node Redistribute instances
Slower than subtree replacement (Worthwhile?) X witten & eibe

Estimating error rates
Prune only if it reduces the estimated error Error on the training data is NOT a useful estimator Q: Why it would result in very little pruning? Use hold-out set for pruning (“reduced-error pruning”) C4.5’s method Derive confidence interval from training data Use a heuristic limit, derived from this, for pruning Standard Bernoulli-process-based method Shaky statistical assumptions (based on training data) witten & eibe

*Mean and variance Mean and variance for a Bernoulli trial: p, p (1–p)
Expected success rate f=S/N Mean and variance for f : p, p (1–p)/N For large enough N, f follows a Normal distribution c% confidence interval [–z  X  z] for random variable with 0 mean is given by: With a symmetric distribution: witten & eibe

*Confidence limits Confidence limits for the normal distribution with 0 mean and a variance of 1: Thus: To use this we have to reduce our random variable f to have 0 mean and unit variance Pr[X  z] z 0.1% 3.09 0.5% 2.58 1% 2.33 5% 1.65 10% 1.28 20% 0.84 25% 0.69 40% 0.25 witten & eibe

*Transforming f Transformed value for f : (i.e. subtract the mean and divide by the standard deviation) Resulting equation: Solving for p: witten & eibe

C4.5’s method Error estimate for subtree is weighted sum of error estimates for all its leaves Error estimate for a node (upper bound): If c = 25% then z = 0.69 (from normal distribution) f is the error on the training data N is the number of instances covered by the leaf witten & eibe

Example f = 5/14 e = 0.46 e < 0.51 so prune! f=0.33 e=0.47
Combined using ratios 6:2:6 gives 0.51 witten & eibe

*Complexity of tree induction
Assume m attributes n training instances tree depth O (log n) Building a tree O (m n log n) Subtree replacement O (n) Subtree raising O (n (log n)2) Every instance may have to be redistributed at every node between its leaf and the root Cost for redistribution (on average): O (log n) Total cost: O (m n log n) + O (n (log n)2) witten & eibe

From trees to rules – how?
How can we produce a set of rules from a decision tree?

From trees to rules – simple
Simple way: one rule for each leaf C4.5rules: greedily prune conditions from each rule if this reduces its estimated error Can produce duplicate rules Check for this at the end Then look at each class in turn consider the rules for that class find a “good” subset (guided by MDL) Then rank the subsets to avoid conflicts Finally, remove rules (greedily) if this decreases error on the training data witten & eibe

C4.5rules: choices and options
C4.5rules slow for large and noisy datasets Commercial version C5.0rules uses a different technique Much faster and a bit more accurate C4.5 has two parameters Confidence value (default 25%): lower values incur heavier pruning Minimum number of instances in the two most popular branches (default 2) witten & eibe

*Classification rules
Common procedure: separate-and-conquer Differences: Search method (e.g. greedy, beam search, ...) Test selection criteria (e.g. accuracy, ...) Pruning method (e.g. MDL, hold-out set, ...) Stopping criterion (e.g. minimum accuracy) Post-processing step Also: Decision list vs. one rule set for each class witten & eibe

*Test selection criteria
Basic covering algorithm: keep adding conditions to a rule to improve its accuracy Add the condition that improves accuracy the most Measure 1: p/t t total instances covered by rule p number of these that are positive Produce rules that don’t cover negative instances, as quickly as possible May produce rules with very small coverage —special cases or noise? Measure 2: Information gain p (log(p/t) – log(P/T)) P and T the positive and total numbers before the new condition was added Information gain emphasizes positive rather than negative instances These interact with the pruning mechanism used witten & eibe

*Missing values, numeric attributes
Common treatment of missing values: for any test, they fail Algorithm must either use other tests to separate out positive instances leave them uncovered until later in the process In some cases it’s better to treat “missing” as a separate value Numeric attributes are treated just like they are in decision trees witten & eibe

*Pruning rules Two main strategies:
Incremental pruning Global pruning Other difference: pruning criterion Error on hold-out set (reduced-error pruning) Statistical significance MDL principle Also: post-pruning vs. pre-pruning witten & eibe

Summary Decision Trees Both C4.5 and CART are robust tools
splits – binary, multi-way split criteria – entropy, gini, … missing value treatment pruning rule extraction from trees Both C4.5 and CART are robust tools No method is always superior – experiment! witten & eibe